Autonomous Wireless Sensor Networks Research Articles

Implementation of a Wireless Sensor Network for Environmental Measurements

Nowadays, the need to monitor different physical variables is constantly increasing and can be used in different applications, from humidity monitoring to disease detection in living beings, using a local or wireless sensor network (WSN). The Internet of Things has become a valuable approach to climate monitoring, daily parcel monitoring, early disease detection, crop plant counting, and risk assessment. Herein, an autonomous energy wireless sensor network for monitoring environmental variables is proposed. The network’s tree topology configuration, which involves master and slave modules, is managed by microcontrollers embedded with sensors, constituting a key part of the WSN architecture. The system’s slave modules are equipped with sensors for temperature, humidity, gas, and light detection, along with a photovoltaic cell to energize the system, and a WiFi module for data transmission. The receiver incorporates a user interface and the necessary computing components for efficient data handling. In an open-field configuration, the transceiver range of the proposed system reaches up to 750 m per module. The advantages of this approach are its scalability, energy efficiency, and the system’s ability to provide real-time environmental monitoring over a large area, which is particularly beneficial for applications in precision agriculture and environmental management.

An efficient trust-based decision-making approach for WSNs: Machine learning oriented approach

Wireless Sensor Networks (WSNs) are often used for critical applications where trust and security are of paramount importance. Trust evaluation is one of the key mechanisms to ensure the security and reliability of WSNs. Traditional trust evaluation schemes rely on fixed, predetermined thresholds, or rules and static attack models, which may not be suitable for all situations such as dynamic and heterogeneous network environments with new and unknown attack scenarios as well as have several problems such as limited security and scalability, limited accuracy, incomplete coverage, lack of adaptability that can limit their effectiveness. Machine Learning (ML) has been shown to be an effective tool for trust evaluation in WSNs, offering several benefits over existing schemes such as greater adaptability, scalability, and accuracy since ML algorithms can analyze and learn from the data collected in real-time from multiple sources (sensor readings, network traffic, and user behavior) enabling them to dynamically adjust their decision-making criteria based on the current network conditions. Trust-aware ML-based security mechanisms achieve safety and efficient decision-making by reducing uncertainty and risk to accomplish real-world tasks. This paper presents a Machine Learning (ML)-based trust evaluation model in the unattended autonomous WSN environment to achieve reliability, adaptability, scalability, and accuracy by generating quick and reliable trust values dynamically. The proposed machine learning algorithm extracts various trust features such as Co-Location Relationship (CLR), Co-Work Relationship (CWR), Cooperativeness-Frequency-Duration (CFD), and Reward (R) to obtain a robust trust rating of sensor devices and predict future misbehavior. These trust features are combined to generate a final trust rating before making any decision about the reliability of any sensor device. Moreover, the projected trust model (ETDMA) integrate direct communication trust and indirect trust with the help of a logical time window that periodically records the trustworthy and suspicious interactions. Simulation experiments exhibit the effectiveness of the proposed trust evaluation method in terms of change in trust values, malicious nodes detection (94%), FNR (0.9%), F1-Score (0.6), and accuracy (92%) in the presence of 50 malicious nodes.

Autonomous Electrical Current Monitoring System for Aircraft

Aircraft monitoring systems offer enhanced safety, reliability, reduced maintenance cost and improved overall flight efficiency. Advancements in wireless sensor networks (WSN) are enabling unprecedented data acquisition functionalities, but their applicability is restricted by power limitations, as batteries require replacement or recharging and wired power adds weight and detracts from the benefits of wireless technology. In this paper, an energy autonomous WSN is presented for monitoring the structural current in aircraft structures. A hybrid inductive/hall sensing concept is introduced demonstrating 0.5 A resolution, < 2% accuracy and frequency independence, for a 5 A – 100 A RMS, DC-800 Hz current and frequency range, with 35 mW active power consumption. An inductive energy harvesting power supply with magnetic flux funnelling, reactance compensation and supercapacitor storage is demonstrated to provide 0.16 mW of continuous power from the 65 μT RMS field of a 20 A RMS, 360 Hz structural current. A low-power sensor node platform with a custom multi-mode duty cycling network protocol is developed, offering cold starting network association and data acquisition/transmission functionality at 50 μW and 70 μW average power respectively. WSN level operation for 1 minute for every 8 minutes of energy harvesting is demonstrated. The proposed system offers a unique energy autonomous WSN platform for aircraft monitoring.

Boosting the energy harvesting performance of cantilever structured magneto-mechano-electric generator by controlling magnetic flux intensity on magnet proof mass

A cantilever-structured magneto-mechano-electric (MME) generator comprising a magnetoelectric composite with a magnet proof mass is a potential candidate for powering autonomous wireless sensor networks. Recently, the concept of a magnetic flux concentrator (MFC) to enhance the output performance of the MME generator by focusing the ultralow-intensity magnetic field into the MME generator was introduced. However, the MFC-concentrated magnetic flux mostly focused on the end tip of the MME cantilever rather than at the magnet proof mass located on the cantilever beam. Considering that the torque generated by the magnet proof mass contributes more than half of the output power of an MME generator, optimizing the volume and position of the proof-mass with MFC is crucial for better performance. Furthermore, a smaller proof-mass is desirable for the long-term reliability of cantilever-type harvesters. Hence, we investigated the effect of the position and weight (volume) of the magnet proof mass with respect to the MFC on the output performance of the MME generator through finite element analysis and experiments. The MME generator with the lighter magnet proof mass at the optimized position generated a maximum power of 5.35 mW under a 10 Oe magnetic field, which was 210% of that of the MME configuration used in our previous study. Furthermore, the MME generator showed broadband characteristics around the practical frequency of 60 Hz, which could provide more freedom to design the harvester with high performance.

WIRELESS SENSOR NETWORK FOR CURRENT AND FUTURE TRENDS: A REVIEW

Wireless Sensor Networks (WSNs) are an emerging technology that has gained immense popularity due to its ability to provide real-time data from remote locations. In recent years, WSNs [1] have been widely adopted in various applications, including environmental monitoring, healthcare, agriculture, smart homes, and smart cities. This paper presents an abstract on the current and future trends of WSNs. One of the significant trends in WSNs is the development of energy-efficient protocols and algorithms. Researchers are exploring new techniques to minimize energy consumption by sensors, which directly impacts the network's lifetime. Another trend is the integration of WSNs with other technologies, such as the Internet of Things (IoT), cloud computing, and big data analytics, to enable more advanced applications. Another trend is the use of Machine Learning (ML) and Artificial Intelligence (AI) techniques to analyze the data collected by WSNs. These techniques can be used to identify patterns and anomalies in the data, which can be used to improve the efficiency and accuracy of the system. The future of WSNs looks promising, with new technologies emerging that will enhance their capabilities. One of the upcoming trends is the development of Low-Power Wide Area Networks (LPWANs), which will enable long-range communication with low power consumption. This technology will be particularly useful in smart cities and industrial applications. The development of autonomous WSNs is another future trend. These networks will be capable of self-organization, self-configuration, and self-management, reducing the need for human intervention. This will result in more efficient and reliable networks. WSNs are an essential technology that has the potential to revolutionize various industries. As the technology continues to evolve, it is essential to keep up with the latest trends to ensure that WSNs remain at the forefront of innovation.

Autonomous WSNs node powered by a thermal energy harvester for temperature monitoring

An autonomous wireless sensor networks (WSNs) node powered by a thermal energy harvester with MPPT is proposed in this paper for temperature monitoring. Two commercial TEG modules are used to build up the thermoelectric collector, while an energy management circuit with a DC/DC converter is employed to boost the TEG output voltage. The harvested energy is stored in a supercapacitor. The feasibility of the presented self-powered WSNs node is verified by a series of experiments with various node sleep periods and temperature differences across the thermoelectric generator (ΔT TEG). The results show that the proposed thermal energy harvester can work at its maximum power point and autonomously power a commercial WSN node when ΔT TEG is above 13.3°C and node sleep period is not less than 1.5 s. In addition, the designed self-powered WSNs node can monitor the temperature of industrial devices successfully.

Applications of wireless sensor networks to improve occupational safety and health in underground mines

Introduction: The very complex and hazardous environment of underground mines may significantly contribute to occupational fatalities and injuries. Deploying wireless sensor network (WSN) technology has the potential to improve safety and health monitoring of miners and operators. However, the application of WSN in the industry is not fully understood and current research themes in this area are fragmented. Thus, there is a need for a comprehensive review that directly explores the contribution of WSNs to occupational safety and health (OSH) in underground mines. Method: This study aims to conduct a systematic literature review on the existing applications of WSNs for improving OSH in the underground mining industry to pinpoint innovative research themes and their main achievements, reveal gaps and shortcomings in the literature, recommend avenues for future scholarly works, and propose potential safety interventions. The major contribution of this review is to provide researchers and practitioners with a holistic understanding of the integration of WSN applications into underground mine safety and health management. Results: The review results have been categorized and discussed under three predominant categories including location monitoring and tracking, physiological and body kinematics monitoring, and environmental monitoring. Finally, seven major directions for future research and practical interventions have been identified based on the existing research gaps including: (1) further applications of WSNs for underground mining OSH management; (2) application of WSNs from research to real-world practice; (3) big data analytics and management; (4) deploying multiple WSNs-based monitoring systems; (5) integration of WSNs with other communication systems; (6) adapting WSNs to the Internet of Things (IoT) infrastructure; and (7) autonomous WSNs.

The Major Mechanisms for Efficient Hybrid Energy Harvesting: Overview and Recent Developments

Devastating environmental issues and the cost of replacement of batteries in autonomous low-powered electrical, electronic, and mechatronic systems, the interest in ambient energy harvesting has witnessed steady growth recently. The maximization and utilization of these eco-friendly energies have given rise to efficient hybrid energy harvesting, which involves the combination of two or more standalone energy harvesting mechanisms such as Vibrational, thermoelectric, pyroelectric, photovoltaic, etc. The comparison of the recent development, applications, and challenges of the major standalone and hybrid harvesting mechanisms in both large and small-scale mechanisms are the main emphasis of this article. Also, this review holistically discussed the latest optimal techniques utilized in hybrid energy harvesting mechanisms for the effective performance of systems and to guarantee stable power to autonomous electronics and wireless sensor networks. The study will help research scholars to understand and focus on the high-potential techniques to achieve maximum power from hybrid harvesters.

Self-Sustained Autonomous Wireless Sensor Network with Integrated Solar Photovoltaic System for Internet of Smart Home-Building (IoSHB) Applications.

Conventional wireless sensor networks (WSNs) in smart home-building (SHB) are typically driven by batteries, limiting their lifespan and the maximum number of deployable units. To satisfy the energy demand for the next generation of SHB which can interconnect WSNs to make the internet of smart home-building (IoSHB), this study introduces the design and implementation of a 250 mW to 2.3 W energy harvesting device. The proposed device is dynamically autonomous owing to the integration of embedded solar photovoltaic (PV) modules and power storage through a supercapacitor (SC; 5 V, 0.47 F) capable of powering WSNs for 95 s (up to 4.11 V). The deployed device can harvest indoor and outdoor ambient light at a minimum illumination of 50 lux and a maximum illumination of 200 lux. Moreover, the proposed system supports wireless fidelity (Wi-Fi) and Bluetooth Low Energy (BLE) to do data transfer to a webserver as a complete internet of things (IoT) device. A customized android dashboard is further developed for data monitoring on a smartphone. All in all, this self-powered WSN node can interface with the users of the SHBs for displaying ambient data, which demonstrates its promising applicability and stability.

An Overview of IoT-Enabled Monitoring and Control Systems for Electric Vehicles

As 5G technology becomes operational and continues to expand, smart cities become a reality that is transforming urban life at a rapid pace. Electric Vehicles (EV) and automated driving, equipped with Battery Energy Storage Systems (BESSs), are expected to dominate public transportation in smart cities. While new technologies can facilitate efficiency and reduce the costs in a city, they can also present challenges. This paper provides an overview of the technical challenges of real-time monitoring and control of Energy Storage Systems (ESSs) for EVs in smart cities. It also covers the Internet-of-the-Things (IoT) technology that can be utilized to address the challenges and improve the efficiency of Battery Management Systems (BMS). Autonomous Wireless Sensor Networks (WSNs) in smart cities provide the infrastructure to support advanced EV features, such as self-parking. IoT sensors can also be used to determine the State-of-Charge (SoC) in EVs by data-driven methods and cloud computing services.

Unknown hostile environment-oriented autonomous WSN deployment using a mobile robot

In this study, we consider the Internet of Things (IoT) with an autonomous deployment framework and seek optimal localizable k-coverage (OLKC) strategies to preserve the connectivity and robustness in IoT networks to assist robots during disaster recovery activities. Therefore, we define localizable k-coverage as the covered region within which a mobile robot can localize itself aided by k neighboring beacon nodes (BNs) in a wireless sensor network (WSN). To this end, we first propose the optimal localizable k-coverage WSN deployment problem (OLKWDP) and present a novel framework that preserves WSN connectivity and robustness for mobile robots. To localize a mobile robot with at least k BNs and overcome the network hole problem that can occur in unknown hostile environments, we propose a hole recovery method for the OLKC achieved by a mobile robot that knows the concurrent mapping, deployment and localization of the WSN. We then present a mapping-to-image transformation method to reveal the interactions between the WSN deployment and the network holes for the OLKC while constructing the online mapping. To solve the OLKWDP, we also develop two optimality conditions to achieve maximum coverage by the proposed OLKC in the unknown hostile environment using the minimum number of sensors. Moreover, we analyze the factors that influence the probability of success of the OLKC and the factors that influence the performance of a mobile robot when determining the WSN deployment. The simulation results illustrate that our framework outperforms the trilateration and spanning tree (TST) method in unknown hostile environment exploration and can achieve the OLKC in a WSN. In 27 simulated situations, our framework achieved average rates of nearly 100% 1-coverage, 91.34% 2-coverage and 89.00% 3-coverage.

Realization of Autonomous Sensor Networks with AI based Self-reconfiguration and Optimal Data Transmission Algorithms in resource constrained nodes

Wireless sensor networks (WSN) prove to be an enabling technology for Industry 4.0 for their ability to perform in autonomous manner even in regions of extreme conditions. Autonomy brings in independent decision making and exerting controls without manual intervention and frequent maintenance. This paper aims to inculcate intelligence to the WSN exploiting the merits of Artificial Intelligence (AI) algorithms in cheap and most preferred ESP8266 and ESP32 based nodes. Autonomy is brought in by means of optimal data transmission, compressive sensing fault detection and network reconfiguration and energy efficiency. Optimal data transmission is achieved using Q-learning based exploration exploitation algorithm. Compressive sensing performed using Autoencoders ensure reduction in transmission overhead. Fault detection is done using Binary SVM classifier and the net- work re-configures based on physical redundancy. This paper high- lights the implementation of such autonomous WSN in real time along with their performance statistics.

Local Seeing Measurement for Increasing Astrophysical Observatory Quality Images Using an Autonomous Wireless Sensor Network.

Astrophysical observatories (AOs) are used to acquire high-quality images from the sky. However, AOs are amenable to distortion phenomena such as seeing. In this paper, we consider specifically the local seeing produced from indoor and outdoor temperature variations. Local seeing contributes to the captured image quality, therefore it must be measured. Local seeing has been considered, to the best of our knowledge, in observatories using ad hoc solutions, typically with high cost and complexity. This paper presents the complete development of an autonomous wireless sensor network (WSN) composed of temperature-measuring for real-time local seeing measurement. Therefore, a WSN is deployed using Zigbee as a data communication exchange. As a result, a long continuous-operating system is constructed and tested in a real AO infrastructure. Finally, we calculate a preliminary dome local seeing, from the acquired temperature data, achieving reasonable results.

Determining the trade-offs between data delivery and energy consumption in large-scale WSNs by multi-objective evolutionary optimization

Autonomous Wireless Sensor Networks (WSNs) form the basis of several Internet of Things (IoT) applications. Efficiency in routing data is a major concern in these networks since sensor nodes are energy-constrained and mostly unaware of the optimal routes regarding a set of QoS metrics. In this paper, the energy consumption and data delivery reliability are deemed the most important factors to the adequate overall network and application operation. Nevertheless, to enhance the chances of data packets to arrive at destinations, in general, energy consumption and reliability have to compromise. Although many routing techniques exist for WSNs, it is hard to determine, in general, if they really can find good solutions considering such trade-off. In light of this, we present a multi-objective integer problem (MOIP) to the routing problem in WSNs to optimize both energy consumption and delivery reliability. Moreover, to solve the proposed MOIP, we introduce an efficient evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The solution method is capable of constructing Pareto set approximations in a low computational time that can be used to better evaluate the solutions provided by routing protocols prior to deployment. The results of experiments with medium-sized and large-scale scenarios based on real case studies indicated the clear compromise between energy efficiency and delivery ratio. Moreover, by the results achieved by NSGA-II, the scalability does not affect the performance of the routing in optimizing the two objectives.

A novel MPPT method for autonomous wireless sensor networks node with thermal energy harvesting

Energy harvesting is a promising alternative to batteries for autonomous WSN nodes. Although there are various energy harvesting methods using ambient energy sources, this research is focused on thermal energy harvesting because there are abundant thermal sources in industrial sites. As only several mW can be obtained by a thermal energy harvester based on a thermoelectric generator (TEG), improvement of the energy conversion efficiency of the thermal energy harvester is essential. This paper proposes a novel maximum power point tracking (MPPT) method combining coarse adjustment and fine adjustment to enhance the energy conversion efficiency of the harvester. The Direct Adjustment (DA) method for MPPT is adopted in the coarse adjustment stage to avoid disconnecting TEG modules from conversion circuits, while an observation and self-optimization (O&SO) method for MPPT is used in the fine adjustment stage to avoid introducing perturbations and to maintain a stable output power at the maximum power point (MPP). After building up the system simulation model, a series of experiments are conducted to evaluate the proposed MPPT methods. The results show that the DA + O&SO method is an effective MPPT method for a WSN node powered by a TEG. It is able to enhance the regulation speed of the MPPT, improve the continuity of Pout, and avoid the power oscillations around the MPP.

LOAF: Load and Resource Aware Federation of Multiple Sensor Sub-Networks

Wireless sensor networks (WSNs) serving in hostile environments are susceptible to multiple collocated failures due to explosives, and natural calamities such as avalanches, landslides etc., which could partition the network into disjoint segments. A similar scenario is encountered when autonomous WSNs need to collaborate for achieving a common task; therefore, federating the segments or individual WSNs would be essential for sharing data between them. The federation may be achieved by populating relay nodes and providing perpetual inter-segment paths. In this paper, we tackle the federation problem while considering constrained relay availability that makes the problem more challenging. We exploit the use of a limited number of mobile relays to provide intermittent inter-segment connectivity. We propose LOAF, a novel algorithm for LOad and resource Aware Federation of multiple sensor sub-networks. LOAF strives to group the segments into multiple clusters considering the amount of energy consumed to support the inter-segment traffic as well as proximity between segments. The formed inter-cluster topology is star-shaped where the segments in each cluster are served by a distinct mobile relay and a central cluster provides inter-cluster data delivery. During forming a central cluster, LOAF opts to balance the load on the individual mobile relays in terms of energy consumed in travel and in wireless communication. We analyze the properties of LOAF mathematically and validate its performance through extensive simulation experiments.

Autonomous Wireless Sensor Networks in an IPM Spatial Decision Support System

Until recently data acquisition in integrated pest management (IPM) relied on manual collection of both pest and environmental data. Autonomous wireless sensor networks (WSN) are providing a way forward by reducing the need for manual offload and maintenance; however, there is still a significant gap in pest management using WSN with most applications failing to provide a low-cost, autonomous monitoring system that can operate in remote areas. In this study, we investigate the feasibility of implementing a reliable, fully independent, low-power WSN that will provide high-resolution, near-real-time input to a spatial decision support system (SDSS), capturing the small-scale heterogeneity needed for intelligent IPM. The WSN hosts a dual-uplink taking advantage of both satellite and terrestrial communication. A set of tests were conducted to assess metrics such as signal strength, data transmission and bandwidth of the SatCom module as well as mesh configuration, energetic autonomy, point to point communication and data loss of the WSN nodes. Finally, we demonstrate the SDSS output from two vector models forced by WSN data from a field site in Belgium. We believe that this system can be a cost-effective solution for intelligent IPM in remote areas where there is no reliable terrestrial connection.

Power Allocation for a Self-Sustainable Power Substation Monitoring System Using Wireless Transfer of Energy

Internet of Things (IoT) can provide an on-line monitoring system for power substations and therefore offer a much more reliable maintenance strategy for power devices. However, energizing the monitoring sensors in a power substation and around High Voltage (HV) devices is difficult and costly. It is possible to reduce the installation costs significantly if the monitoring sensors can be self-sustainable. In this work, we study an autonomous Wireless Sensor Network(WSN), based on energy harvesting and wireless transfer of energy. In order to optimize the performance of this system, we propose and analyze two power allocation techniques, aiming to energize the monitoring sensors wirelessly. This paper concludes that by optimizing the parameters of the system, a self-sustainable WSN in a power substation can be successfully deployed.

Integration of Autonomous Wireless Sensor Networks in Academic School Gardens.

In this work, the combination of capabilities provided by Wireless Sensor Networks (WSN) with parameter observation in a school garden is employed in order to provide an environment for school garden integration as a complementary educational activity in primary schools. Wireless transceivers with energy harvesting capabilities are employed in order to provide autonomous system operation, combined with an ad-hoc implemented application called MySchoolGardenApp, based on a modular software architecture. The system enables direct parameter observation, data analysis and processing capabilities, which can be employed by students in a cloud based platform. Providing remote data access allows the adaptation of content to specific classroom/homework needs. The proposed monitoring WSN has been deployed in an orchard located in the schoolyard of a primary school, which has been built with EnOcean’s energy harvesting modules, providing an optimized node device as well network layout. For the assessment of the wireless link quality and the deployment of the modules, especially the central module which needs to receive directly the signals of all the sensor modules, simulation results obtained by an in-house developed 3D Ray Launching deterministic method have been used, providing coverage/capacity estimations applicable to the specific school environment case. Preliminary trials with MySchoolGardenApp have been performed, showing the feasibility of the proposed platform as an educational resource in schools, with application in specific natural science course content, development of technological skills and the extension of monitoring capabilities to new context-aware applications.

Autonomous Wireless Sensor Networks: Implementation of Transient Computing and Energy Prediction for Improved Node Performance and Link Quality

Over the last decades, research and development in energy harvesting significantly changed the traditional way of powering electronic devices, as exemplified by the emergence of battery-less wireless sensor nodes. In contrast to existing works that consider energy prediction and transient computing separately, we present a novel approach for the joint implementation of these two modalities aimed at improving the adaptability and robustness of applications running on battery-less nodes. The proposed approach performs online energy measurements and store them as energy profiles. Based on these, we use our recently proposed LINE-P (Case-II) model to estimate the energy availability in the near future. These energy estimates are exploited in a transient computing mechanism that manages the operation of the nodes, in particular their wireless communication functions. We present an implementation thereof for a peer-to-peer application on FRAM-based wireless sensor nodes (TI MSP430FR5739 MCU and TI CC2500 transceiver) that operate without energy storage. In a first set of experiments, the nodes are powered by a power supply programmed to mimic the intermittent nature of harvested energy; in a second set, they are powered by a solar panel yielding real datasets (profiles) at 1, 5 and 10-min data intervals. The experimental results show how the proposed combined transient computing and energy prediction approach addresses various issues such as time failure of the node and system shutdown, as well as how the link quality and overall performance of the peer-to-peer application are improved.